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Spectral fingerprinting of ovarian cancer in serum samples

Ovarian cancer can be predicted with high sensitivity and specificity via a fingerprint obtained, via machine learning, from near-infrared fluorescence emissions of an array of carbon nanotube sensors in serum samples.
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Go to the profile of Pep Pàmies
Pep Pàmies
Chief Editor, Nature Biomedical Engineering, Nature Research
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Nature Nature

Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning - Nature Biomedical Engineering

Ovarian cancer can be predicted with high sensitivity and specificity via a fingerprint obtained, via machine learning, from near-infrared fluorescence emissions of an array of carbon nanotube sensors in serum samples.

The cover illustrates that the analysis, via machine learning, of near-infrared-fluorescence emissions of carbon-nanotube sensors placed in serum samples can be used to predict ovarian cancer.

See Kim et al.

Image: Olga Kharchenko. Cover design: Alex Wing.
Go to the profile of Pep Pàmies
Pep Pàmies
Chief Editor, Nature Biomedical Engineering, Nature Research

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